Water Quality Modeling of the River Ganga in the Northern Region of India Using the Artificial Neural Network Technique

Abstract

Water quality modeling with dynamic parameters, especially of rivers, is important in terms of proactive pollution management strategies. Techniques such as artificial neural networks (ANNs) have become popular for such applications. In the present study, an ANN is used to construct a multilayer perceptron and radial basis function neural network model to simulate and predict dissolved oxygen in the River Ganga in selected regions of Uttar Pradesh, and to demonstrate its application in identifying complex nonlinear relationships between input and output variables. The results of the model analysis demonstrate that the multi-layer perceptron model provides greater correlation coefficients (R = 0. 993) and a lower mean square error (RMSE = 0. 1984) than the radial basis function model (R = 0.789; RMSE = 1.0011). The results of the analysis suggest the suitability of the proposed MLP-ANN model to predict water quality parameters such as dissolved oxygen using limiting data sets for the River Ganga, in particular, and other rivers in general.